Accuracy of Touch-Based Registration During Robotic Image-Guided Partial Nephrectomy Before and After Tumor Resection in Validated Phantoms

Patient-specific simulations and navigation systems for partial nephrectomy

Partial nephrectomy (PN) is the standard treatment for T1 renal cell carcinoma. PN is affected more by surgical variations and requires greater surgical experience than radical nephrectomy. Patient-specific simulations and navigation systems may help to reduce the surgical experience required for PN. Recent advances in three-dimensional (3D) virtual reality (VR) imaging and 3D printing technology have allowed accurate patient-specific simulations and navigation systems. We reviewed previous studies about patient-specific simulations and navigation systems for PN. Recently, image reconstruction technology has developed, and commercial software that converts two-dimensional images into 3D images has become available. Many urologists are now able to view 3DVR images when preparing for PN. Surgical simulations based on 3DVR images can change surgical plans and improve surgical outcomes, and are useful during patient consultations. Patient-specific simulators that are capable of simulating surgical procedures, the gold-standard form of patient-specific simulations, have also been reported. Besides VR, 3D printing is also useful for understanding patient-specific information. Some studies have reported simulation and navigation systems for PN based on solid 3D models. Patient-specific simulations are a form of preoperative preparation, whereas patient-specific navigation is used intraoperatively. Navigation-assisted PN procedures using 3DVR images have become increasingly common, especially in robotic surgery. Some studies found that these systems produced improvements in surgical outcomes. Once its accuracy has been confirmed, it is hoped that this technology will spread further and become more generalized.

A Safe Framework for Quantitative In Vivo Human Evaluation of Image Guidance

Goal: We present a new framework for in vivo image guidance evaluation and provide a case study on robotic partial nephrectomy. Methods: This framework (called the "bystander protocol") involves two surgeons, one who solely performs the therapeutic process without image guidance, and another who solely periodically collects data to evaluate image guidance. This isolates the evaluation from the therapy, so that in-development image guidance systems can be tested without risk of negatively impacting the standard of care. We provide a case study applying this protocol in clinical cases during robotic partial nephrectomy surgery. Results: The bystander protocol was performed successfully in 6 patient cases. We find average lesion centroid localization error with our IGS system to be 6.5 mm in vivo compared to our prior result of 3.0 mm in phantoms. Conclusions: The bystander protocol is a safe, effective method for testing in-development image guidance systems in human subjects.

New imaging technologies for robotic kidney cancer surgery

Objective

Kidney cancers account for approximately 2% of all newly diagnosed cancer in 2020. Among the primary treatment options for kidney cancer, urologist may choose between radical or partial nephrectomy, or ablative therapies. Nowadays, robotic-assisted partial nephrectomy (RAPN) for the management of renal cancers has gained popularity, up to being considered the gold standard. However, RAPN is a challenging procedure with a steep learning curve.

Methods

In this narrative review, different imaging technologies used to guide and aid RAPN are discussed.

Results

Three-dimensional visualization technology has been extensively discussed in RAPN, showing its value in enhancing robotic-surgery training, patient counseling, surgical planning, and intraoperative guidance. Intraoperative imaging technologies such as intracorporeal ultrasound, near-infrared fluorescent imaging, and intraoperative pathological examination can also be used to improve the outcomes following RAPN. Finally, artificial intelligence may play a role in the field of RAPN soon.

Conclusion

RAPN is a complex surgery; however, many imaging technologies may play an important role in facilitating it.

Robotic partial nephrectomy in 3D virtual reconstructions era: is the paradigm changed?

CONTEXT

The development of a tailored, patient-specific medical and surgical approach is becoming object of intense research. In kidney oncologic surgery, where a clear understanding of case-specific surgical anatomy is considered a key point to optimize the perioperative outcomes, such philosophy gained increasing importance. Recently, important advances in 3D virtual modeling technologies have fueled the interest for their application in the field of robotic minimally invasive surgery for kidney tumors.

OBJECTIVE

To provide a synthesis of current applications of 3D virtual models for robot-assisted partial nephrectomy.

EVIDENCE ACQUISITION

Medline, PubMed, the Cochrane Database, and Embase were screened for Literature regarding the use of 3D virtual models for robot-assisted partial nephrectomy (RAPN).

EVIDENCE SYNTHESIS

The use of 3D virtual models for RAPN has been tested in different settings, including surgical indication and planning, intraoperative guidance, and training. Currently, several studies are available on the application of this technology for surgical planning, demonstrating impact on clinical outcomes such as renal function recovery, whilst experiences concerning their intraoperative application for navigation are still experimental. One of the latest innovations in this field is represented by the development of dedicated softwares able to automatically overlap the 3D virtual models to the real anatomy, to perform augmented reality procedures.

CONCLUSIONS

The available Literature suggests a potentially crucial role of 3D virtual reconstructions during RAPN. Encouraging results concerning surgical planning and indication, intraoperative navigation, and surgical training are available. In the future, artificial intelligence may represent the key to further improve the 3D virtual modeling technology during RAPN.

Patient-specific, touch-based registration during robotic, image-guided partial nephrectomy

Image-guidance during partial nephrectomy enables navigation within the operative field alongside a 3-dimensional roadmap of renal anatomy generated from patient-specific imaging. Once a process is performed by the human mind, the technology will allow standardization of the task for the benefit of all patients undergoing robot-assisted partial nephrectomy. Any surgeon will be able to visualize the kidney and key subsurface landmarks in real-time within a 3-dimensional simulation, with the goals of improving operative efficiency, decreasing surgical complications, and improving oncologic outcomes. For similar purposes, image-guidance has already been adopted as a standard of care in other surgical fields; we are now at the brink of this in urology. This review summarizes touch-based approaches to image-guidance during partial nephrectomy, as the technology begins to enter in vivo human evaluation. The processes of segmentation, localization, registration, and re-registration are all described with seamless integration into the da Vinci surgical system; this will facilitate clinical adoption sooner.

Machine learning applications to enhance patient specific care for urologic surgery

PURPOSE

As computational power has improved over the past 20 years, the daily application of machine learning methods has become more prevalent in daily life. Additionally, there is increasing interest in the clinical application of machine learning techniques. We sought to review the current literature regarding machine learning applications for patient-specific urologic surgical care.

METHODS

We performed a broad search of the current literature via the PubMed-Medline and Google Scholar databases up to Dec 2020. The search terms "urologic surgery" as well as "artificial intelligence", "machine learning", "neural network", and "automation" were used.

RESULTS

The focus of machine learning applications for patient counseling is disease-specific. For stone disease, multiple studies focused on the prediction of stone-free rate based on preoperative characteristics of clinical and imaging data. For kidney cancer, many studies focused on advanced imaging analysis to predict renal mass pathology preoperatively. Machine learning applications in prostate cancer could provide for treatment counseling as well as prediction of disease-specific outcomes. Furthermore, for bladder cancer, the reviewed studies focus on staging via imaging, to better counsel patients towards neoadjuvant chemotherapy. Additionally, there have been many efforts on automatically segmenting and matching preoperative imaging with intraoperative anatomy.

CONCLUSION

Machine learning techniques can be implemented to assist patient-centered surgical care and increase patient engagement within their decision-making processes. As data sets improve and expand, especially with the transition to large-scale EHR usage, these tools will improve in efficacy and be utilized more frequently.